code: def check_for_failure(remote_values):
      """Checks remote values for any that returned and failed.
      :param remote_values: List of object IDs representing functions
              that may fail in the middle of execution. For example, running
              a SGD training loop in multiple parallel actor calls.
      :return Bool for success in executing given remote tasks.
      """
      unfinished = remote_values
      try:
          while len(unfinished) > 0:
              finished, unfinished = ray.wait(unfinished)
              finished = ray.get(finished)
          return True
      except RayActorError as exc:
          logger.exception(str(exc))
      return False
  
imports: import io
  import types
  import logging
  import numbers
  import torch
  import numpy
  from zoo.orca.data.ray_xshards import RayXShards
  from zoo.orca.learn.pytorch.training_operator import TrainingOperator
  from zoo.orca.learn.pytorch.torch_runner import TorchRunner
  from zoo.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe
  from zoo.ray import RayContext
  import ray
  from ray.exceptions import RayActorError
label_desc: Use image node_id 1 for calling a global function with example usage: check_for_failure(remote_values) and returns: False, True